






import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    FocalNetBackbone,
							    FocalNetForImageClassification,
							    FocalNetForMaskedImageModeling,
							    FocalNetModel,
							)
							from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class 				lowerCAmelCase_	:


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=1_3 ,						_lowerCAmelCase=3_2 ,						_lowerCAmelCase=2 ,						_lowerCAmelCase=3 ,						_lowerCAmelCase=1_6 ,						_lowerCAmelCase=[3_2, 6_4, 1_2_8] ,						_lowerCAmelCase=[1, 2, 1] ,						_lowerCAmelCase=[2, 2, 4] ,						_lowerCAmelCase=2 ,						_lowerCAmelCase=2.0 ,						_lowerCAmelCase=True ,						_lowerCAmelCase=0.0 ,						_lowerCAmelCase=0.0 ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase="gelu" ,						_lowerCAmelCase=False ,						_lowerCAmelCase=True ,						_lowerCAmelCase=0.02 ,						_lowerCAmelCase=1E-5 ,						_lowerCAmelCase=True ,						_lowerCAmelCase=None ,						_lowerCAmelCase=True ,						_lowerCAmelCase=1_0 ,						_lowerCAmelCase=8 ,						_lowerCAmelCase=["stage1", "stage2"] ,						_lowerCAmelCase=[1, 2] ,						):
											_lowercase							:   int 							=   parent
											_lowercase							:   str 							=   batch_size
											_lowercase							:   Optional[Any] 							=   image_size
											_lowercase							:   Union[str, Any] 							=   patch_size
											_lowercase							:   Optional[Any] 							=   num_channels
											_lowercase							:   List[str] 							=   embed_dim
											_lowercase							:   Optional[Any] 							=   hidden_sizes
											_lowercase							:   Dict 							=   depths
											_lowercase							:   int 							=   num_heads
											_lowercase							:   Union[str, Any] 							=   window_size
											_lowercase							:   Optional[int] 							=   mlp_ratio
											_lowercase							:   Dict 							=   qkv_bias
											_lowercase							:   Tuple 							=   hidden_dropout_prob
											_lowercase							:   Optional[int] 							=   attention_probs_dropout_prob
											_lowercase							:   Any 							=   drop_path_rate
											_lowercase							:   Optional[Any] 							=   hidden_act
											_lowercase							:   List[Any] 							=   use_absolute_embeddings
											_lowercase							:   List[Any] 							=   patch_norm
											_lowercase							:   Optional[Any] 							=   layer_norm_eps
											_lowercase							:   Optional[Any] 							=   initializer_range
											_lowercase							:   int 							=   is_training
											_lowercase							:   List[str] 							=   scope
											_lowercase							:   List[str] 							=   use_labels
											_lowercase							:   Tuple 							=   type_sequence_label_size
											_lowercase							:   str 							=   encoder_stride
											_lowercase							:   Dict 							=   out_features
											_lowercase							:   Optional[int] 							=   out_indices


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

											_lowercase							:   Dict 							=   None
											if self.use_labels:
																		_lowercase							:   Optional[Any] 							=   ids_tensor([self.batch_size] ,						self.type_sequence_label_size      )

											_lowercase							:   List[Any] 							=   self.get_config()

											return config, pixel_values, labels


				def 						__a					(					self      ):
											return FocalNetConfig(
											    image_size=self.image_size ,						patch_size=self.patch_size ,						num_channels=self.num_channels ,						embed_dim=self.embed_dim ,						hidden_sizes=self.hidden_sizes ,						depths=self.depths ,						num_heads=self.num_heads ,						window_size=self.window_size ,						mlp_ratio=self.mlp_ratio ,						qkv_bias=self.qkv_bias ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						drop_path_rate=self.drop_path_rate ,						hidden_act=self.hidden_act ,						use_absolute_embeddings=self.use_absolute_embeddings ,						path_norm=self.patch_norm ,						layer_norm_eps=self.layer_norm_eps ,						initializer_range=self.initializer_range ,						encoder_stride=self.encoder_stride ,						out_features=self.out_features ,						out_indices=self.out_indices ,						)


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Optional[Any] 							=   FocalNetModel(config=_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   int 							=   model(_lowerCAmelCase      )

											_lowercase							:   List[str] 							=   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
											_lowercase							:   Any 							=   int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

											self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, expected_seq_len, expected_dim)      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Optional[int] 							=   FocalNetBackbone(config=_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   Dict 							=   model(_lowerCAmelCase      )

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps      ) ,						len(config.out_features      )      )
											self.parent.assertListEqual(list(result.feature_maps[0].shape      ) ,						[self.batch_size, self.image_size, 8, 8]      )

											# verify channels
											self.parent.assertEqual(len(model.channels      ) ,						len(config.out_features      )      )
											self.parent.assertListEqual(model.channels ,						config.hidden_sizes[:-1]      )

											# verify backbone works with out_features=None
											_lowercase							:   List[str] 							=   None
											_lowercase							:   Optional[Any] 							=   FocalNetBackbone(config=_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   int 							=   model(_lowerCAmelCase      )

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps      ) ,						1      )
											self.parent.assertListEqual(list(result.feature_maps[0].shape      ) ,						[self.batch_size, self.image_size * 2, 4, 4]      )

											# verify channels
											self.parent.assertEqual(len(model.channels      ) ,						1      )
											self.parent.assertListEqual(model.channels ,						[config.hidden_sizes[-1]]      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Any 							=   FocalNetForMaskedImageModeling(config=_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   Union[str, Any] 							=   model(_lowerCAmelCase      )
											self.parent.assertEqual(
											    result.reconstruction.shape ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

											# test greyscale images
											_lowercase							:   Optional[int] 							=   1
											_lowercase							:   List[str] 							=   FocalNetForMaskedImageModeling(_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()

											_lowercase							:   Optional[int] 							=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
											_lowercase							:   Optional[int] 							=   model(_lowerCAmelCase      )
											self.parent.assertEqual(result.reconstruction.shape ,						(self.batch_size, 1, self.image_size, self.image_size)      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Optional[int] 							=   self.type_sequence_label_size
											_lowercase							:   str 							=   FocalNetForImageClassification(_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()
											_lowercase							:   Any 							=   model(_lowerCAmelCase ,						labels=_lowerCAmelCase      )
											self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.type_sequence_label_size)      )

											# test greyscale images
											_lowercase							:   Dict 							=   1
											_lowercase							:   Tuple 							=   FocalNetForImageClassification(_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()

											_lowercase							:   List[Any] 							=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
											_lowercase							:   int 							=   model(_lowerCAmelCase      )
											self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.type_sequence_label_size)      )


				def 						__a					(					self      ):
											_lowercase							:   int 							=   self.prepare_config_and_inputs()

											_lowercase							,							_lowercase							,							_lowercase							:   Tuple 							=   config_and_inputs
											_lowercase							:   Dict 							=   {'pixel_values': pixel_values}
											return config, inputs_dict




@require_torch
class 				lowerCAmelCase_	(		__snake_case   ,		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       int							       = (
				    (
				        FocalNetModel,
				        FocalNetForImageClassification,
				        FocalNetForMaskedImageModeling,
				        FocalNetBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				_UpperCamelCase					:       List[str]							       = (
				    {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
				    if is_torch_available()
				    else {}
				)
				_UpperCamelCase					:       int							       = False

				_UpperCamelCase					:       Any							       = False
				_UpperCamelCase					:       List[str]							       = False
				_UpperCamelCase					:       Union[str, Any]							       = False
				_UpperCamelCase					:       Optional[int]							       = False


				def 						__a					(					self      ):
											_lowercase							:   int 							=   FocalNetModelTester(self      )
											_lowercase							:   int 							=   ConfigTester(self ,						config_class=_lowerCAmelCase ,						embed_dim=3_7 ,						has_text_modality=_lowerCAmelCase      )


				def 						__a					(					self      ):
											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()


				def 						__a					(					self      ):
											return


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   Any 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   Optional[Any] 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   int 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase      )


				@unittest.skip(reason='FocalNet does not use inputs_embeds'      )
				def 						__a					(					self      ):
											pass


				@unittest.skip(reason='FocalNet does not use feedforward chunking'      )
				def 						__a					(					self      ):
											pass


				def 						__a					(					self      ):
											_lowercase							,							_lowercase							:   int 							=   self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes[:-1]:
																		_lowercase							:   Optional[Any] 							=   model_class(_lowerCAmelCase      )
																		self.assertIsInstance(model.get_input_embeddings() ,						(nn.Module)      )
																		_lowercase							:   Any 							=   model.get_output_embeddings()
																		self.assertTrue(x is None or isinstance(_lowerCAmelCase ,						nn.Linear      )      )


				def 						__a					(					self      ):
											_lowercase							,							_lowercase							:   Optional[int] 							=   self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes[:-1]:
																		_lowercase							:   Union[str, Any] 							=   model_class(_lowerCAmelCase      )
																		_lowercase							:   Any 							=   inspect.signature(model.forward      )
																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
																		_lowercase							:   str 							=   [*signature.parameters.keys()]

																		_lowercase							:   int 							=   ['pixel_values']
																		self.assertListEqual(arg_names[:1] ,						_lowerCAmelCase      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   List[Any] 							=   model_class(_lowerCAmelCase      )
											model.to(_lowerCAmelCase      )
											model.eval()

											with torch.no_grad():
																		_lowercase							:   Any 							=   model(**self._prepare_for_class(_lowerCAmelCase ,						_lowerCAmelCase      )      )

											_lowercase							:   Any 							=   outputs.hidden_states

											_lowercase							:   List[Any] 							=   getattr(
											    self.model_tester ,						'expected_num_hidden_layers' ,						len(self.model_tester.depths      ) + 1      )
											self.assertEqual(len(_lowerCAmelCase      ) ,						_lowerCAmelCase      )

											# FocalNet has a different seq_length
											_lowercase							:   Tuple 							=   (
											    config.patch_size
											    if isinstance(config.patch_size ,						collections.abc.Iterable      )
											    else (config.patch_size, config.patch_size)
											)

											_lowercase							:   Union[str, Any] 							=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]      ) ,						[num_patches, self.model_tester.embed_dim] ,						)

											_lowercase							:   Union[str, Any] 							=   outputs.reshaped_hidden_states
											self.assertEqual(len(_lowerCAmelCase      ) ,						_lowerCAmelCase      )

											_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							:   Optional[int] 							=   reshaped_hidden_states[0].shape
											_lowercase							:   Optional[int] 							=   (
											    reshaped_hidden_states[0].view(_lowerCAmelCase ,						_lowerCAmelCase ,						height * width      ).permute(0 ,						2 ,						1      )
											)
											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-2:]      ) ,						[num_patches, self.model_tester.embed_dim] ,						)


				def 						__a					(					self      ):
											_lowercase							,							_lowercase							:   Tuple 							=   self.model_tester.prepare_config_and_inputs_for_common()

											_lowercase							:   Optional[int] 							=   (
											    self.model_tester.image_size
											    if isinstance(self.model_tester.image_size ,						collections.abc.Iterable      )
											    else (self.model_tester.image_size, self.model_tester.image_size)
											)

											for model_class in self.all_model_classes[:-1]:
																		_lowercase							:   Tuple 							=   True
																		self.check_hidden_states_output(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      )

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		_lowercase							:   Dict 							=   True

																		self.check_hidden_states_output(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							,							_lowercase							:   Any 							=   self.model_tester.prepare_config_and_inputs_for_common()
											_lowercase							:   Optional[Any] 							=   3

											_lowercase							:   Tuple 							=   (
											    self.model_tester.image_size
											    if isinstance(self.model_tester.image_size ,						collections.abc.Iterable      )
											    else (self.model_tester.image_size, self.model_tester.image_size)
											)
											_lowercase							:   Tuple 							=   (
											    config.patch_size
											    if isinstance(config.patch_size ,						collections.abc.Iterable      )
											    else (config.patch_size, config.patch_size)
											)

											_lowercase							:   Any 							=   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
											_lowercase							:   Union[str, Any] 							=   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

											for model_class in self.all_model_classes[:-1]:
																		_lowercase							:   List[Any] 							=   True
																		self.check_hidden_states_output(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						(padded_height, padded_width)      )

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		_lowercase							:   Optional[Any] 							=   True
																		self.check_hidden_states_output(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						(padded_height, padded_width)      )


				@slow
				def 						__a					(					self      ):
											for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_lowercase							:   List[str] 							=   FocalNetModel.from_pretrained(_lowerCAmelCase      )
																		self.assertIsNotNone(_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							,							_lowercase							:   List[Any] 							=   self.model_tester.prepare_config_and_inputs_for_common()

											_lowercase							:   Tuple 							=   _config_zero_init(_lowerCAmelCase      )
											for model_class in self.all_model_classes:
																		_lowercase							:   Optional[Any] 							=   model_class(config=_lowerCAmelCase      )
																		for name, param in model.named_parameters():
																									if "embeddings" not in name and param.requires_grad:
																																self.assertIn(
																																    ((param.data.mean() * 1E9).round() / 1E9).item() ,						[0.0, 1.0] ,						msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" ,						)




@require_vision
@require_torch
class 				lowerCAmelCase_	(		unittest.TestCase			):


				@cached_property
				def 						__a					(					self      ):
											# TODO update organization
											return AutoImageProcessor.from_pretrained('microsoft/focalnet-tiny'      ) if is_vision_available() else None


				@slow
				def 						__a					(					self      ):
											_lowercase							:   str 							=   FocalNetForImageClassification.from_pretrained('microsoft/focalnet-tiny'      ).to(_lowerCAmelCase      )
											_lowercase							:   Optional[int] 							=   self.default_image_processor

											_lowercase							:   Tuple 							=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
											_lowercase							:   List[Any] 							=   image_processor(images=_lowerCAmelCase ,						return_tensors='pt'      ).to(_lowerCAmelCase      )

											# forward pass
											with torch.no_grad():
																		_lowercase							:   int 							=   model(**_lowerCAmelCase      )

											# verify the logits
											_lowercase							:   Dict 							=   torch.Size((1, 1_0_0_0)      )
											self.assertEqual(outputs.logits.shape ,						_lowerCAmelCase      )
											_lowercase							:   Any 							=   torch.tensor([0.21_66, -0.43_68, 0.21_91]      ).to(_lowerCAmelCase      )
											self.assertTrue(torch.allclose(outputs.logits[0, :3] ,						_lowerCAmelCase ,						atol=1E-4      )      )
											self.assertTrue(outputs.logits.argmax(dim=-1      ).item() ,						2_8_1      )




@require_torch
class 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       List[str]							       = (FocalNetBackbone,) if is_torch_available() else ()
				_UpperCamelCase					:       Dict							       = FocalNetConfig

				_UpperCamelCase					:       Optional[Any]							       = False


				def 						__a					(					self      ):
											_lowercase							:   str 							=   FocalNetModelTester(self      )












'''simple docstring'''





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def __UpperCAmelCase   (					A					:			Optional[int]		)    ->   List[Any]:
			# This defines a "chinese character" as anything in the CJK Unicode block:
			#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
			#
			# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
			# despite its name. The modern Korean Hangul alphabet is a different block,
			# as is Japanese Hiragana and Katakana. Those alphabets are used to write
			# space-separated words, so they are not treated specially and handled
			# like the all of the other languages.
			if (
			    (cp >= 0X4E_00 and cp <= 0X9F_FF)
			    or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
			    or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
			    or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
			    or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
			    or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
			    or (cp >= 0XF9_00 and cp <= 0XFA_FF)
			    or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
			):  #
						return True

			return False
def __UpperCAmelCase   (					A					:			str		)    ->   Optional[Any]:
			# word like '180' or '身高' or '神'
			for char in word:
						UpperCAmelCase_      :       str          =       ord(A		)
						if not _is_chinese_char(A		):
									return 0
			return 1
def __UpperCAmelCase   (					A					:			List[str]		)    ->   Dict:
			UpperCAmelCase_      :       Optional[Any]          =       set()

			for token in tokens:
						UpperCAmelCase_      :       str          =       len(A		) > 1 and is_chinese(A		)
						if chinese_word:
									word_set.add(A		)
			UpperCAmelCase_      :       Optional[int]          =       list(A		)
			return word_list
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			set()		)    ->   Optional[Any]:
			if not chinese_word_set:
						return bert_tokens
			UpperCAmelCase_      :       Dict          =       max([len(A		) for w in chinese_word_set]		)

			UpperCAmelCase_      :       List[str]          =       bert_tokens
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       0, len(A		)
			while start < end:
						UpperCAmelCase_      :       str          =       True
						if is_chinese(bert_word[start]		):
									UpperCAmelCase_      :       str          =       min(end - start			,       A		)
									for i in range(A			,       1			,       -1		):
												UpperCAmelCase_      :       Tuple          =       ''''''.join(bert_word[start : start + i]		)
												if whole_word in chinese_word_set:
															for j in range(start + 1			,       start + i		):
																		UpperCAmelCase_      :       Union[str, Any]          =       '''##''' + bert_word[j]
															UpperCAmelCase_      :       Any          =       start + i
															UpperCAmelCase_      :       Optional[int]          =       False
															break
						if single_word:
									start += 1
			return bert_word
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			LTP			,       A					:			BertTokenizer		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Optional[int]          =       []

			for i in range(0			,       len(A		)			,       1_0_0		):
						UpperCAmelCase_      :       int          =       ltp_tokenizer.pipeline(lines[i : i + 1_0_0]			,       tasks=['''cws''']		).cws
						UpperCAmelCase_      :       Any          =       [get_chinese_word(A		) for r in res]
						ltp_res.extend(A		)
			assert len(A		) == len(A		)

			UpperCAmelCase_      :       Tuple          =       []
			for i in range(0			,       len(A		)			,       1_0_0		):
						UpperCAmelCase_      :       Optional[int]          =       bert_tokenizer(lines[i : i + 1_0_0]			,       add_special_tokens=A			,       truncation=A			,       max_length=5_1_2		)
						bert_res.extend(res['''input_ids''']		)
			assert len(A		) == len(A		)

			UpperCAmelCase_      :       Any          =       []
			for input_ids, chinese_word in zip(A			,       A		):
						UpperCAmelCase_      :       Union[str, Any]          =       []
						for id in input_ids:
									UpperCAmelCase_      :       Union[str, Any]          =       bert_tokenizer._convert_id_to_token(A		)
									input_tokens.append(A		)
						UpperCAmelCase_      :       List[str]          =       add_sub_symbol(A			,       A		)
						UpperCAmelCase_      :       Any          =       []
						# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
						for i, token in enumerate(A		):
									if token[:2] == "##":
												UpperCAmelCase_      :       int          =       token[2:]
												# save chinese tokens' pos
												if len(A		) == 1 and _is_chinese_char(ord(A		)		):
															ref_id.append(A		)
						ref_ids.append(A		)

			assert len(A		) == len(A		)

			return ref_ids
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Tuple:
			# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
			# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
			with open(args.file_name			,       '''r'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       Optional[Any]          =       f.readlines()
			UpperCAmelCase_      :       List[str]          =       [line.strip() for line in data if len(A		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			UpperCAmelCase_      :       Tuple          =       LTP(args.ltp		)  # faster in GPU device
			UpperCAmelCase_      :       Union[str, Any]          =       BertTokenizer.from_pretrained(args.bert		)

			UpperCAmelCase_      :       Optional[int]          =       prepare_ref(A			,       A			,       A		)

			with open(args.save_path			,       '''w'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       str          =       [json.dumps(A		) + '''\n''' for ref in ref_ids]
						f.writelines(A		)


if __name__ == "__main__":
						_UpperCamelCase						:						Optional[Any]  =					argparse.ArgumentParser(description='prepare_chinese_ref')
						parser.add_argument(
						    '--file_name',
						    required=False,
						    type=str,
						    default='./resources/chinese-demo.txt',
						    help='file need process, same as training data in lm',
						)
						parser.add_argument(
						    '--ltp',
						    required=False,
						    type=str,
						    default='./resources/ltp',
						    help='resources for LTP tokenizer, usually a path',
						)
						parser.add_argument(
						    '--bert',
						    required=False,
						    type=str,
						    default='./resources/robert',
						    help='resources for Bert tokenizer',
						)
						parser.add_argument(
						    '--save_path',
						    required=False,
						    type=str,
						    default='./resources/ref.txt',
						    help='path to save res',
						)

						_UpperCamelCase						:						Any  =					parser.parse_args()
						main(args)


